Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Recent advances in the reinforcement learning (RL) literature have enabled roboticists to
automatically train complex policies in simulated environments. However, due to the poor …

Reinforcement learning (RL) provides a theoretical framework for continuously improving an
agent's behavior via trial and error. However, efficiently learning policies from scratch can be …

Exploration strategy design is one of the challenging problems in reinforcement
learning~(RL), especially when the environment contains a large state space or sparse …

The approach of directly combining clustering method and reinforcement learning (RL) will
lead to encounter the issue where states may have different state transition processes under …

State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly
interact with their environment to collect millions of observations. This makes it hard to …

In reinforcement learning (RL) research, simulations enable benchmarks between
algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote …

Despite significant progress in challenging problems across various domains, applying state-
of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their …

Model-based reinforcement learning is a compelling framework for data-efficient learning of
agents that interact with the world. This family of algorithms has many subcomponents that …

In this article, a novel reinforcement learning (RL) approach, continuous dynamic policy
programming (CDPP), is proposed to tackle the issues of both learning stability and sample …